convnext

core/train/train_convnext_tiny.py

@@ -0,0 +1,53 @@
+import os
+import torch
+from core.nets.convnext_tiny import pytorch_convnext_tiny
+from core.dataloader.dataloader import train_dataloader
+from core.const import epoch, lr, batch_size
+
+
+def train():
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("device: ", device)
+
+    net = pytorch_convnext_tiny().to(device)
+
+    loss_func = torch.nn.CrossEntropyLoss()
+
+    optimizer = torch.optim.Adam(net.parameters(), lr=lr)
+
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=20, eta_min=1e-6)
+
+    for e in range(epoch):
+        print("epoch: ", e)
+        net.train()
+
+        for i, data in enumerate(train_dataloader):
+            inputs, labels = data
+            inputs, labels = inputs.to(device), labels.to(device)
+
+            outputs = net(inputs)
+
+            loss = loss_func(outputs, labels)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            _, pred = torch.max(outputs, dim=1)
+            correct = pred.eq(labels.data).cpu().sum()
+
+            print("step: ", i, "loss: ", loss.item(), "correct: ", 1.0 * correct / batch_size)
+
+        scheduler.step()
+        print("lr: ", optimizer.state_dict()['param_groups'][0]['lr'])
+
+        script_dir = os.path.dirname(os.path.abspath(__file__))
+        model_dir = os.path.join(script_dir, "..", "models")
+        if not os.path.exists(model_dir):
+            os.makedirs(model_dir)
+
+        torch.save(net.state_dict(), os.path.join(model_dir, "convnext_tiny_epoch_{}.pth".format(e + 1)))
+
+
+if __name__ == "__main__":
+    train()